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import gradio as gr
import pandas as pd
import numpy as np
import pickle
import os
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from utils import (plot_distances_tsne,
plot_distances_umap,
cluster_languages_hdbscan,
cluster_languages_kmeans,
plot_mst,
cluster_languages_by_families,
cluster_languages_by_subfamilies,
filter_languages_by_families)
from functools import partial
import datasets
dataset = datasets.load_dataset("mshamrai/language-metric-data", split="train", trust_remote_code=True)
languages = dataset["languages_list"][0]
average_distances_matrix = np.array(dataset["average_distances_matrix"][0])
DATASETS = dataset["distances_matrices"][0]["dataset_name"]
MODELS = dataset["distances_matrices"][0]["models"][0]["model_name"]
distance_matrices = {
DATASETS[i]: {
MODELS[j]: np.array(dataset["distances_matrices"][0]["models"][i]["matrix"][j])
for j in range(len(MODELS))
}
for i in range(len(DATASETS))
}
def filter_languages_nan(model, dataset, use_average):
if use_average:
matrix = average_distances_matrix
else:
matrix = distance_matrices[dataset][model]
vector = matrix[0]
updated_languages = np.array(languages)[~np.isnan(vector)]
updated_matrix = matrix[~np.isnan(vector), :][:, ~np.isnan(vector)]
return updated_matrix, updated_languages
def get_similar_languages(model, dataset, selected_language, use_average, n):
"""
Retrieves the distances for the selected language from the chosen model and dataset,
sorts them by similarity (lowest distance first), and returns a DataFrame.
"""
if use_average:
matrix = average_distances_matrix
else:
matrix = distance_matrices[dataset][model]
selected_language_index = languages.index(selected_language)
distances = matrix[selected_language_index]
df = pd.DataFrame({"Language": languages, "Distance": distances})
sorted_distances = df.sort_values(by="Distance")
sorted_distances.drop(index=selected_language_index, inplace=True)
sorted_distances.reset_index(drop=True, inplace=True)
sorted_distances.reset_index(inplace=True)
sorted_distances["Distance"] = sorted_distances["Distance"].round(4)
return sorted_distances.head(n)
def update_languages(model, dataset):
"""
Returns the language list based on the given model and dataset.
"""
matrix = distance_matrices[dataset][model]
vector = matrix[0]
updated_languages = np.array(languages)[~np.isnan(vector)]
return list(updated_languages)
def update_language_options(model, dataset, language, use_average):
if use_average:
updated_languages = languages
else:
updated_languages = update_languages(model, dataset)
if language not in updated_languages:
language = updated_languages[0]
return gr.Dropdown(label="Language", choices=updated_languages, value=language)
def toggle_inputs(use_average):
if use_average:
return gr.update(interactive=False, visible=False), gr.update(interactive=False, visible=False)
else:
return gr.update(interactive=True, visible=True), gr.update(interactive=True, visible=True)
plot_path = "plots/last_plot.pdf"
def plot_distances(model, dataset, use_average, cluster_method, cluster_method_param, plot_fn):
"""
Plots all languages from the distances matrix using t-SNE.
"""
updated_matrix, updated_languages = filter_languages_nan(model, dataset, use_average)
if cluster_method == "HDBSCAN":
filtered_matrix, filtered_languages, clusters = cluster_languages_hdbscan(
updated_matrix, updated_languages, min_cluster_size=cluster_method_param
)
legends = None
elif cluster_method == "KMeans":
filtered_matrix, filtered_languages, clusters = cluster_languages_kmeans(
updated_matrix, updated_languages, n_clusters=cluster_method_param
)
legends = None
elif cluster_method == "Family":
clusters, legends = cluster_languages_by_families(updated_languages)
filtered_matrix = updated_matrix
filtered_languages = updated_languages
elif cluster_method == "Subfamily":
clusters, legends = cluster_languages_by_subfamilies(updated_languages)
filtered_matrix = updated_matrix
filtered_languages = updated_languages
else:
raise ValueError("Invalid cluster method")
fig = plot_fn(model, dataset, use_average, filtered_matrix, filtered_languages, clusters, legends)
fig.tight_layout()
fig.savefig(plot_path, format="pdf")
return fig, gr.DownloadButton(label="Download Plot", value=plot_path)
def plot_families_subfamilies(families, model, dataset, use_average, figsize_h, figsize_w):
updated_matrix, updated_languages = filter_languages_nan(model, dataset, use_average)
updated_matrix, updated_languages = filter_languages_by_families(updated_matrix, updated_languages, families)
clusters, legends = cluster_languages_by_subfamilies(updated_languages)
fig = plot_mst(model, dataset, use_average, updated_matrix, updated_languages, clusters, legends, fig_size=(figsize_w, figsize_h))
fig.tight_layout()
fig.savefig(plot_path, format="pdf")
return fig, gr.DownloadButton(label="Download Plot", value=plot_path)
with gr.Blocks() as demo:
gr.Markdown("## Language Distance Explorer")
average_checkbox = gr.Checkbox(label="Use Average Distances", value=False)
with gr.Row():
model_input = gr.Dropdown(label="Model", choices=MODELS, value=MODELS[0])
dataset_input = gr.Dropdown(
label="Dataset",
choices=DATASETS,
value=DATASETS[0]
)
with gr.Tab(label="Closest Languages Table"):
with gr.Row():
language_input = gr.Dropdown(label="Language", choices=languages, value=languages[0])
top_n_input = gr.Slider(label="Top N", minimum=1, maximum=30, step=1, value=10)
output_table = gr.Dataframe(label="Similar Languages")
model_input.change(fn=update_language_options, inputs=[model_input, dataset_input, language_input, average_checkbox], outputs=language_input)
dataset_input.change(fn=update_language_options, inputs=[model_input, dataset_input, language_input, average_checkbox], outputs=language_input)
language_input.change(fn=get_similar_languages, inputs=[model_input, dataset_input, language_input, average_checkbox, top_n_input], outputs=output_table)
model_input.change(fn=get_similar_languages, inputs=[model_input, dataset_input, language_input, average_checkbox, top_n_input], outputs=output_table)
dataset_input.change(fn=get_similar_languages, inputs=[model_input, dataset_input, language_input, average_checkbox, top_n_input], outputs=output_table)
top_n_input.change(fn=get_similar_languages, inputs=[model_input, dataset_input, language_input, average_checkbox, top_n_input], outputs=output_table)
average_checkbox.change(
fn=toggle_inputs,
inputs=[average_checkbox],
outputs=[model_input, dataset_input]
)
average_checkbox.change(fn=update_language_options, inputs=[model_input, dataset_input, language_input, average_checkbox], outputs=language_input)
average_checkbox.change(fn=get_similar_languages, inputs=[model_input, dataset_input, language_input, average_checkbox, top_n_input], outputs=output_table)
with gr.Tab(label="Distance Plot"):
with gr.Row():
cluster_method_input = gr.Dropdown(label="Cluster Method", choices=["HDBSCAN", "KMeans", "Family", "Subfamily"], value="HDBSCAN")
clusters_input = gr.Slider(label="Minimum Elements in a Cluster", minimum=2, maximum=10, step=1, value=2)
def update_clusters_input_option(cluster_method):
if cluster_method == "HDBSCAN":
return gr.Slider(label="Minimum Elements in a Cluster", minimum=2, maximum=10, step=1, value=2, visible=True, interactive=True)
elif cluster_method == "KMeans":
return gr.Slider(label="Number of Clusters", minimum=2, maximum=20, step=1, value=2, visible=True, interactive=True)
else:
return gr.update(interactive=False, visible=False)
cluster_method_input.change(fn=update_clusters_input_option, inputs=[cluster_method_input], outputs=clusters_input)
with gr.Row():
plot_tsne_button = gr.Button("Plot t-SNE")
plot_umap_button = gr.Button("Plot UMAP")
plot_mst_button = gr.Button("Plot MST")
with gr.Row():
download_plot_button = gr.DownloadButton("Download Plot")
with gr.Row():
plot_output = gr.Plot(label="Distance Plot")
plot_tsne_button.click(fn=partial(plot_distances, plot_fn=plot_distances_tsne),
inputs=[model_input, dataset_input, average_checkbox, cluster_method_input, clusters_input],
outputs=[plot_output, download_plot_button])
plot_umap_button.click(fn=partial(plot_distances, plot_fn=plot_distances_umap),
inputs=[model_input, dataset_input, average_checkbox, cluster_method_input, clusters_input],
outputs=[plot_output, download_plot_button])
plot_mst_button.click(fn=partial(plot_distances, plot_fn=plot_mst),
inputs=[model_input, dataset_input, average_checkbox, cluster_method_input, clusters_input],
outputs=[plot_output, download_plot_button])
with gr.Tab(label="Language Families Subplot"):
checked_families_input = gr.CheckboxGroup(label="Language Families",
choices=[
'Afroasiatic',
'Austroasiatic',
'Austronesian',
'Constructed',
'Creole',
'Dravidian',
'Germanic',
'Indo-European',
'Japonic',
'Kartvelian',
'Koreanic',
'Language Isolate',
'Niger-Congo',
'Northeast Caucasian',
'Romance',
'Sino-Tibetan',
'Turkic',
'Uralic'
],
value=["Indo-European"])
with gr.Row():
plot_family_button = gr.Button("Plot Families")
plot_figsize_h_input = gr.Slider(label="Figure Height", minimum=5, maximum=30, step=1, value=15)
plot_figsize_w_input = gr.Slider(label="Figure Width", minimum=5, maximum=30, step=1, value=15)
with gr.Row():
download_families_plot_button = gr.DownloadButton("Download Plot", value=plot_path)
plot_family_output = gr.Plot(label="Families Plot")
plot_family_button.click(fn=plot_families_subfamilies,
inputs=[checked_families_input, model_input, dataset_input, average_checkbox, plot_figsize_h_input, plot_figsize_w_input],
outputs=[plot_family_output, download_families_plot_button])
demo.launch(share=True)
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